Abstract

In this paper, we present a Context Aware Thai Tourism Recommender System (CAT-TOURS) that applies a complex Naïve Bayes Model with boundary values, tourism ontology for Thailand and a temporal ontology to support decision making in tourism. Promising results are presented in the form of precision, recall and F measure for Websites related to Thailand’s tourism industry. We compare the results with those gained with Latent Semantic Indexing (LSI).

This research was guided by the following aims: (1) find a simple method to classify Thai tourism Web documents that contain information on more than one topic, and (2) take into account time constraints in the process of making recommendations.